Overview

Dataset statistics

Number of variables55
Number of observations15120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Alerts

Soil_Type15 has constant value ""Constant
Aspect is highly overall correlated with Hillshade_3pmHigh correlation
Cover_Type is highly overall correlated with Soil_Type10 and 3 other fieldsHigh correlation
Elevation is highly overall correlated with Horizontal_Distance_To_Fire_Points and 4 other fieldsHigh correlation
Hillshade_3pm is highly overall correlated with Aspect and 2 other fieldsHigh correlation
Hillshade_9am is highly overall correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly overall correlated with Hillshade_3pm and 1 other fieldsHigh correlation
Horizontal_Distance_To_Fire_Points is highly overall correlated with ElevationHigh correlation
Horizontal_Distance_To_Hydrology is highly overall correlated with Vertical_Distance_To_HydrologyHigh correlation
Horizontal_Distance_To_Roadways is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Slope is highly overall correlated with Hillshade_NoonHigh correlation
Soil_Type10 is highly overall correlated with Cover_TypeHigh correlation
Soil_Type29 is highly overall correlated with Wilderness_Area1High correlation
Soil_Type3 is highly overall correlated with Cover_TypeHigh correlation
Soil_Type40 is highly overall correlated with ElevationHigh correlation
Vertical_Distance_To_Hydrology is highly overall correlated with Horizontal_Distance_To_HydrologyHigh correlation
Wilderness_Area1 is highly overall correlated with Cover_Type and 1 other fieldsHigh correlation
Wilderness_Area3 is highly overall correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area4 is highly overall correlated with Cover_Type and 3 other fieldsHigh correlation
Wilderness_Area2 is highly imbalanced (76.9%)Imbalance
Soil_Type1 is highly imbalanced (84.5%)Imbalance
Soil_Type2 is highly imbalanced (75.1%)Imbalance
Soil_Type3 is highly imbalanced (64.7%)Imbalance
Soil_Type4 is highly imbalanced (69.1%)Imbalance
Soil_Type5 is highly imbalanced (90.6%)Imbalance
Soil_Type6 is highly imbalanced (73.6%)Imbalance
Soil_Type7 is highly imbalanced (99.9%)Imbalance
Soil_Type8 is highly imbalanced (99.8%)Imbalance
Soil_Type9 is highly imbalanced (99.6%)Imbalance
Soil_Type11 is highly imbalanced (83.2%)Imbalance
Soil_Type12 is highly imbalanced (87.5%)Imbalance
Soil_Type13 is highly imbalanced (78.6%)Imbalance
Soil_Type14 is highly imbalanced (91.0%)Imbalance
Soil_Type16 is highly imbalanced (94.0%)Imbalance
Soil_Type17 is highly imbalanced (74.7%)Imbalance
Soil_Type18 is highly imbalanced (97.1%)Imbalance
Soil_Type19 is highly imbalanced (96.6%)Imbalance
Soil_Type20 is highly imbalanced (92.8%)Imbalance
Soil_Type21 is highly imbalanced (99.2%)Imbalance
Soil_Type22 is highly imbalanced (84.8%)Imbalance
Soil_Type23 is highly imbalanced (71.8%)Imbalance
Soil_Type24 is highly imbalanced (87.3%)Imbalance
Soil_Type25 is highly imbalanced (99.5%)Imbalance
Soil_Type26 is highly imbalanced (96.9%)Imbalance
Soil_Type27 is highly imbalanced (99.3%)Imbalance
Soil_Type28 is highly imbalanced (99.4%)Imbalance
Soil_Type29 is highly imbalanced (57.5%)Imbalance
Soil_Type30 is highly imbalanced (71.9%)Imbalance
Soil_Type31 is highly imbalanced (85.8%)Imbalance
Soil_Type32 is highly imbalanced (74.0%)Imbalance
Soil_Type33 is highly imbalanced (75.3%)Imbalance
Soil_Type34 is highly imbalanced (98.7%)Imbalance
Soil_Type35 is highly imbalanced (94.1%)Imbalance
Soil_Type36 is highly imbalanced (98.9%)Imbalance
Soil_Type37 is highly imbalanced (97.8%)Imbalance
Soil_Type38 is highly imbalanced (71.7%)Imbalance
Soil_Type39 is highly imbalanced (74.9%)Imbalance
Soil_Type40 is highly imbalanced (80.5%)Imbalance
Horizontal_Distance_To_Hydrology has 1506 (10.0%) zerosZeros
Vertical_Distance_To_Hydrology has 1801 (11.9%) zerosZeros

Reproduction

Analysis started2024-02-08 16:48:33.312510
Analysis finished2024-02-08 16:48:50.488874
Duration17.18 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ)

HIGH CORRELATION 

Distinct1676
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2748.6499
Minimum1877
Maximum3850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:50.554554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1877
5-th percentile2115
Q12373
median2754
Q33109
95-th percentile3397
Maximum3850
Range1973
Interquartile range (IQR)736

Descriptive statistics

Standard deviation419.00959
Coefficient of variation (CV)0.15244196
Kurtosis-1.0884902
Mean2748.6499
Median Absolute Deviation (MAD)370
Skewness0.074424175
Sum41559587
Variance175569.04
MonotonicityNot monotonic
2024-02-08T17:48:50.654989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2821 27
 
0.2%
2797 23
 
0.2%
2328 22
 
0.1%
2850 22
 
0.1%
2332 22
 
0.1%
3372 22
 
0.1%
2702 21
 
0.1%
2972 21
 
0.1%
3393 21
 
0.1%
2397 21
 
0.1%
Other values (1666) 14898
98.5%
ValueCountFrequency (%)
1877 1
 
< 0.1%
1889 1
 
< 0.1%
1890 1
 
< 0.1%
1896 3
< 0.1%
1899 1
 
< 0.1%
1906 2
< 0.1%
1911 3
< 0.1%
1913 1
 
< 0.1%
1914 2
< 0.1%
1915 1
 
< 0.1%
ValueCountFrequency (%)
3850 1
< 0.1%
3849 1
< 0.1%
3848 1
< 0.1%
3845 2
< 0.1%
3840 1
< 0.1%
3836 1
< 0.1%
3835 1
< 0.1%
3829 2
< 0.1%
3821 1
< 0.1%
3819 2
< 0.1%

Aspect
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.83452
Minimum0
Maximum360
Zeros101
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:50.769216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q165
median125
Q3257
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)192

Descriptive statistics

Standard deviation109.74537
Coefficient of variation (CV)0.704243
Kurtosis-1.1221267
Mean155.83452
Median Absolute Deviation (MAD)76
Skewness0.46644858
Sum2356218
Variance12044.047
MonotonicityNot monotonic
2024-02-08T17:48:50.886134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 128
 
0.8%
90 114
 
0.8%
0 101
 
0.7%
135 89
 
0.6%
72 84
 
0.6%
108 83
 
0.5%
63 82
 
0.5%
18 82
 
0.5%
104 79
 
0.5%
101 78
 
0.5%
Other values (351) 14200
93.9%
ValueCountFrequency (%)
0 101
0.7%
1 34
 
0.2%
2 41
0.3%
3 51
0.3%
4 47
0.3%
5 55
0.4%
6 67
0.4%
7 43
0.3%
8 53
0.4%
9 58
0.4%
ValueCountFrequency (%)
360 2
 
< 0.1%
359 39
0.3%
358 43
0.3%
357 47
0.3%
356 53
0.4%
355 63
0.4%
354 57
0.4%
353 47
0.3%
352 55
0.4%
351 57
0.4%

Slope
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.556746
Minimum0
Maximum50
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:50.985384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.95
Q110
median15
Q322
95-th percentile32
Maximum50
Range50
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.5346018
Coefficient of variation (CV)0.51547579
Kurtosis-0.25286265
Mean16.556746
Median Absolute Deviation (MAD)6
Skewness0.53256718
Sum250338
Variance72.839428
MonotonicityNot monotonic
2024-02-08T17:48:51.089075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 748
 
4.9%
12 718
 
4.7%
13 708
 
4.7%
10 703
 
4.6%
9 688
 
4.6%
15 672
 
4.4%
14 636
 
4.2%
16 626
 
4.1%
8 614
 
4.1%
17 586
 
3.9%
Other values (41) 8421
55.7%
ValueCountFrequency (%)
0 11
 
0.1%
1 55
 
0.4%
2 134
 
0.9%
3 235
 
1.6%
4 321
2.1%
5 395
2.6%
6 460
3.0%
7 560
3.7%
8 614
4.1%
9 688
4.6%
ValueCountFrequency (%)
50 1
 
< 0.1%
49 5
 
< 0.1%
48 2
 
< 0.1%
47 2
 
< 0.1%
46 7
< 0.1%
45 7
< 0.1%
44 5
 
< 0.1%
43 11
0.1%
42 8
0.1%
41 16
0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct397
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.37652
Minimum0
Maximum1376
Zeros1506
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:51.187494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median180
Q3330
95-th percentile636
Maximum1376
Range1376
Interquartile range (IQR)263

Descriptive statistics

Standard deviation209.19638
Coefficient of variation (CV)0.91601527
Kurtosis2.5208781
Mean228.37652
Median Absolute Deviation (MAD)120
Skewness1.4388585
Sum3453053
Variance43763.126
MonotonicityNot monotonic
2024-02-08T17:48:51.287855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1506
 
10.0%
30 1220
 
8.1%
150 539
 
3.6%
60 508
 
3.4%
42 440
 
2.9%
67 419
 
2.8%
108 389
 
2.6%
85 367
 
2.4%
90 330
 
2.2%
120 289
 
1.9%
Other values (387) 9113
60.3%
ValueCountFrequency (%)
0 1506
10.0%
30 1220
8.1%
42 440
 
2.9%
60 508
 
3.4%
67 419
 
2.8%
85 367
 
2.4%
90 330
 
2.2%
95 273
 
1.8%
108 389
 
2.6%
120 289
 
1.9%
ValueCountFrequency (%)
1376 1
< 0.1%
1355 1
< 0.1%
1269 1
< 0.1%
1259 1
< 0.1%
1234 1
< 0.1%
1201 1
< 0.1%
1190 1
< 0.1%
1189 1
< 0.1%
1187 2
< 0.1%
1181 1
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct433
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.311706
Minimum-135
Maximum570
Zeros1801
Zeros (%)11.9%
Negative1147
Negative (%)7.6%
Memory size118.2 KiB
2024-02-08T17:48:51.388650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-135
5-th percentile-5
Q15
median32
Q380
95-th percentile176
Maximum570
Range705
Interquartile range (IQR)75

Descriptive statistics

Standard deviation61.520488
Coefficient of variation (CV)1.1989562
Kurtosis3.2213024
Mean51.311706
Median Absolute Deviation (MAD)32
Skewness1.5099205
Sum775833
Variance3784.7704
MonotonicityNot monotonic
2024-02-08T17:48:51.503945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1801
 
11.9%
5 226
 
1.5%
4 207
 
1.4%
6 204
 
1.3%
7 197
 
1.3%
2 179
 
1.2%
9 174
 
1.2%
13 172
 
1.1%
8 168
 
1.1%
11 168
 
1.1%
Other values (423) 11624
76.9%
ValueCountFrequency (%)
-135 1
< 0.1%
-130 1
< 0.1%
-118 1
< 0.1%
-114 1
< 0.1%
-113 1
< 0.1%
-111 1
< 0.1%
-110 1
< 0.1%
-108 1
< 0.1%
-106 1
< 0.1%
-105 1
< 0.1%
ValueCountFrequency (%)
570 1
 
< 0.1%
551 1
 
< 0.1%
410 1
 
< 0.1%
402 1
 
< 0.1%
401 3
< 0.1%
396 1
 
< 0.1%
395 1
 
< 0.1%
391 2
< 0.1%
387 1
 
< 0.1%
386 1
 
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ)

HIGH CORRELATION 

Distinct3274
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1717.9777
Minimum0
Maximum6803
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:51.619677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile242
Q1760
median1315
Q32292
95-th percentile4624
Maximum6803
Range6803
Interquartile range (IQR)1532

Descriptive statistics

Standard deviation1330.2635
Coefficient of variation (CV)0.77431939
Kurtosis1.0309417
Mean1717.9777
Median Absolute Deviation (MAD)689
Skewness1.2477486
Sum25975823
Variance1769600.8
MonotonicityNot monotonic
2024-02-08T17:48:51.720614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 75
 
0.5%
240 53
 
0.4%
618 47
 
0.3%
900 46
 
0.3%
390 43
 
0.3%
750 42
 
0.3%
277 41
 
0.3%
300 41
 
0.3%
450 39
 
0.3%
120 39
 
0.3%
Other values (3264) 14654
96.9%
ValueCountFrequency (%)
0 8
 
0.1%
30 17
0.1%
42 4
 
< 0.1%
60 19
0.1%
67 9
 
0.1%
85 14
 
0.1%
90 25
0.2%
95 22
0.1%
108 31
0.2%
120 39
0.3%
ValueCountFrequency (%)
6803 1
< 0.1%
6764 1
< 0.1%
6717 1
< 0.1%
6632 1
< 0.1%
6595 1
< 0.1%
6574 1
< 0.1%
6557 1
< 0.1%
6518 1
< 0.1%
6503 1
< 0.1%
6454 1
< 0.1%

Hillshade_9am
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.02884
Minimum52
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:51.820476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile151.95
Q1197
median220
Q3236
95-th percentile251
Maximum254
Range202
Interquartile range (IQR)39

Descriptive statistics

Standard deviation30.638406
Coefficient of variation (CV)0.14382281
Kurtosis1.0729703
Mean213.02884
Median Absolute Deviation (MAD)19
Skewness-1.0754914
Sum3220996
Variance938.71191
MonotonicityNot monotonic
2024-02-08T17:48:51.936604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 285
 
1.9%
226 280
 
1.9%
230 264
 
1.7%
224 257
 
1.7%
227 252
 
1.7%
236 242
 
1.6%
234 234
 
1.5%
229 233
 
1.5%
233 231
 
1.5%
232 231
 
1.5%
Other values (166) 12611
83.4%
ValueCountFrequency (%)
52 1
< 0.1%
56 1
< 0.1%
68 1
< 0.1%
69 1
< 0.1%
71 1
< 0.1%
72 2
< 0.1%
75 1
< 0.1%
76 1
< 0.1%
77 1
< 0.1%
81 2
< 0.1%
ValueCountFrequency (%)
254 185
1.2%
253 208
1.4%
252 203
1.3%
251 209
1.4%
250 213
1.4%
249 224
1.5%
248 204
1.3%
247 200
1.3%
246 181
1.2%
245 188
1.2%

Hillshade_Noon
Real number (ℝ)

HIGH CORRELATION 

Distinct140
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.86574
Minimum99
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:52.035833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile174
Q1207
median223
Q3235
95-th percentile250
Maximum254
Range155
Interquartile range (IQR)28

Descriptive statistics

Standard deviation22.797288
Coefficient of variation (CV)0.10416106
Kurtosis1.0263935
Mean218.86574
Median Absolute Deviation (MAD)14
Skewness-0.94274704
Sum3309250
Variance519.71635
MonotonicityNot monotonic
2024-02-08T17:48:52.152709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
228 331
 
2.2%
225 331
 
2.2%
226 322
 
2.1%
224 309
 
2.0%
232 303
 
2.0%
231 301
 
2.0%
218 298
 
2.0%
229 295
 
2.0%
223 294
 
1.9%
221 289
 
1.9%
Other values (130) 12047
79.7%
ValueCountFrequency (%)
99 2
< 0.1%
102 1
 
< 0.1%
107 2
< 0.1%
111 2
< 0.1%
112 1
 
< 0.1%
115 1
 
< 0.1%
118 1
 
< 0.1%
119 1
 
< 0.1%
120 1
 
< 0.1%
121 3
< 0.1%
ValueCountFrequency (%)
254 134
0.9%
253 143
0.9%
252 157
1.0%
251 162
1.1%
250 163
1.1%
249 178
1.2%
248 189
1.2%
247 195
1.3%
246 212
1.4%
245 210
1.4%

Hillshade_3pm
Real number (ℝ)

HIGH CORRELATION 

Distinct248
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.47712
Minimum0
Maximum251
Zeros95
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:52.253415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q1106
median138
Q3166
95-th percentile207
Maximum251
Range251
Interquartile range (IQR)60

Descriptive statistics

Standard deviation46.070054
Coefficient of variation (CV)0.34258657
Kurtosis-0.060996158
Mean134.47712
Median Absolute Deviation (MAD)30
Skewness-0.35341849
Sum2033294
Variance2122.4499
MonotonicityNot monotonic
2024-02-08T17:48:52.353303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 178
 
1.2%
143 166
 
1.1%
146 165
 
1.1%
149 162
 
1.1%
142 161
 
1.1%
139 159
 
1.1%
135 151
 
1.0%
132 151
 
1.0%
148 148
 
1.0%
128 148
 
1.0%
Other values (238) 13531
89.5%
ValueCountFrequency (%)
0 95
0.6%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 1
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 5
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
251 1
 
< 0.1%
249 1
 
< 0.1%
248 1
 
< 0.1%
246 1
 
< 0.1%
245 2
 
< 0.1%
244 6
< 0.1%
243 3
< 0.1%
242 3
< 0.1%
241 4
< 0.1%
240 5
< 0.1%

Horizontal_Distance_To_Fire_Points
Real number (ℝ)

HIGH CORRELATION 

Distinct2764
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1527.3578
Minimum0
Maximum7095
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:52.453459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile295
Q1750
median1266
Q32002
95-th percentile3747
Maximum7095
Range7095
Interquartile range (IQR)1252

Descriptive statistics

Standard deviation1116.637
Coefficient of variation (CV)0.73109064
Kurtosis3.5414672
Mean1527.3578
Median Absolute Deviation (MAD)592
Skewness1.6516837
Sum23093650
Variance1246878.2
MonotonicityNot monotonic
2024-02-08T17:48:52.573861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
541 49
 
0.3%
618 47
 
0.3%
819 45
 
0.3%
607 45
 
0.3%
242 45
 
0.3%
335 44
 
0.3%
997 40
 
0.3%
342 39
 
0.3%
872 39
 
0.3%
900 38
 
0.3%
Other values (2754) 14689
97.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
30 18
0.1%
42 7
 
< 0.1%
60 7
 
< 0.1%
67 20
0.1%
85 6
 
< 0.1%
90 14
0.1%
95 21
0.1%
108 24
0.2%
120 10
0.1%
ValueCountFrequency (%)
7095 1
< 0.1%
7061 1
< 0.1%
6961 1
< 0.1%
6960 1
< 0.1%
6906 1
< 0.1%
6895 1
< 0.1%
6881 1
< 0.1%
6811 1
< 0.1%
6810 1
< 0.1%
6809 1
< 0.1%

Wilderness_Area1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
11552 
1
3568 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Length

2024-02-08T17:48:52.672258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:52.755380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11552
76.4%
1 3568
 
23.6%

Wilderness_Area2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14551 
1
 
569

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Length

2024-02-08T17:48:52.822574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:52.903798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14551
96.2%
1 569
 
3.8%

Wilderness_Area3
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
8818 
1
6302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Length

2024-02-08T17:48:52.971742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.054276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Most occurring characters

ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8818
58.3%
1 6302
41.7%

Wilderness_Area4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
10439 
1
4681 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Length

2024-02-08T17:48:53.136230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.202583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Most occurring characters

ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10439
69.0%
1 4681
31.0%

Soil_Type1
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14781 
1
 
339

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Length

2024-02-08T17:48:53.289833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.354763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14781
97.8%
1 339
 
2.2%

Soil_Type2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14493 
1
 
627

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Length

2024-02-08T17:48:53.434759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.503961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14493
95.9%
1 627
 
4.1%

Soil_Type3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14114 
1
 
1006

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Length

2024-02-08T17:48:53.588008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.655402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14114
93.3%
1 1006
 
6.7%

Soil_Type4
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14281 
1
 
839

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Length

2024-02-08T17:48:53.738770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.801060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14281
94.5%
1 839
 
5.5%

Soil_Type5
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14939 
1
 
181

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Length

2024-02-08T17:48:53.871717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:53.956400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14939
98.8%
1 181
 
1.2%

Soil_Type6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14441 
1
 
679

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Length

2024-02-08T17:48:54.021324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:54.103575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14441
95.5%
1 679
 
4.5%

Soil_Type7
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15119 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Length

2024-02-08T17:48:54.185327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:54.256993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15119
> 99.9%
1 1
 
< 0.1%

Soil_Type8
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15118 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Length

2024-02-08T17:48:54.338259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:54.405588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15118
> 99.9%
1 2
 
< 0.1%

Soil_Type9
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15116 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Length

2024-02-08T17:48:54.758084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:54.837236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15116
> 99.9%
1 4
 
< 0.1%

Soil_Type10
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
13024 
1
2096 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Length

2024-02-08T17:48:54.906432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:54.986379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13024
86.1%
1 2096
 
13.9%

Soil_Type11
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14744 
1
 
376

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Length

2024-02-08T17:48:55.071841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.135198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14744
97.5%
1 376
 
2.5%

Soil_Type12
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14860 
1
 
260

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Length

2024-02-08T17:48:55.218163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.288992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14860
98.3%
1 260
 
1.7%

Soil_Type13
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14607 
1
 
513

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Length

2024-02-08T17:48:55.373746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.441139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14607
96.6%
1 513
 
3.4%

Soil_Type14
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14947 
1
 
173

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Length

2024-02-08T17:48:55.520130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.590848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14947
98.9%
1 173
 
1.1%

Soil_Type15
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15120 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
100.0%

Length

2024-02-08T17:48:55.671212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.746597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
100.0%

Most occurring characters

ValueCountFrequency (%)
0 15120
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
100.0%

Soil_Type16
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15014 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Length

2024-02-08T17:48:55.822867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:55.900478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15014
99.3%
1 106
 
0.7%

Soil_Type17
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14480 
1
 
640

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Length

2024-02-08T17:48:55.968500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.038702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14480
95.8%
1 640
 
4.2%

Soil_Type18
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15076 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Length

2024-02-08T17:48:56.122478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.201659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15076
99.7%
1 44
 
0.3%

Soil_Type19
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15067 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Length

2024-02-08T17:48:56.269258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.355255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15067
99.6%
1 53
 
0.4%

Soil_Type20
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14988 
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Length

2024-02-08T17:48:56.417550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.501406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14988
99.1%
1 132
 
0.9%

Soil_Type21
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15110 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Length

2024-02-08T17:48:56.571938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.634459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15110
99.9%
1 10
 
0.1%

Soil_Type22
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14788 
1
 
332

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Length

2024-02-08T17:48:56.716237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.787057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14788
97.8%
1 332
 
2.2%

Soil_Type23
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14378 
1
 
742

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Length

2024-02-08T17:48:56.872385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:56.950511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14378
95.1%
1 742
 
4.9%

Soil_Type24
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14855 
1
 
265

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Length

2024-02-08T17:48:57.019801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.089890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14855
98.2%
1 265
 
1.8%

Soil_Type25
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15114 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Length

2024-02-08T17:48:57.166664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.236262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15114
> 99.9%
1 6
 
< 0.1%

Soil_Type26
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15072 
1
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Length

2024-02-08T17:48:57.319992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.382947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15072
99.7%
1 48
 
0.3%

Soil_Type27
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15112 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Length

2024-02-08T17:48:57.462931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.537349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15112
99.9%
1 8
 
0.1%

Soil_Type28
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15113 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Length

2024-02-08T17:48:57.603732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.684351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15113
> 99.9%
1 7
 
< 0.1%

Soil_Type29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
13812 
1
 
1308

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Length

2024-02-08T17:48:57.763225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.838169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13812
91.3%
1 1308
 
8.7%

Soil_Type30
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14384 
1
 
736

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Length

2024-02-08T17:48:57.915583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:57.987401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14384
95.1%
1 736
 
4.9%

Soil_Type31
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14816 
1
 
304

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Length

2024-02-08T17:48:58.064440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.121368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14816
98.0%
1 304
 
2.0%

Soil_Type32
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14457 
1
 
663

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Length

2024-02-08T17:48:58.202559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.269234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14457
95.6%
1 663
 
4.4%

Soil_Type33
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14501 
1
 
619

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Length

2024-02-08T17:48:58.347414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.418209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14501
95.9%
1 619
 
4.1%

Soil_Type34
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15102 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Length

2024-02-08T17:48:58.503368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.572667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15102
99.9%
1 18
 
0.1%

Soil_Type35
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15017 
1
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Length

2024-02-08T17:48:58.638117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.720710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15017
99.3%
1 103
 
0.7%

Soil_Type36
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15106 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Length

2024-02-08T17:48:58.804370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:58.868340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15106
99.9%
1 14
 
0.1%

Soil_Type37
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
15088 
1
 
32

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Length

2024-02-08T17:48:58.936246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:59.019167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15088
99.8%
1 32
 
0.2%

Soil_Type38
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14376 
1
 
744

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Length

2024-02-08T17:48:59.087317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:59.169367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14376
95.1%
1 744
 
4.9%

Soil_Type39
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14486 
1
 
634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Length

2024-02-08T17:48:59.237857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:59.316672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14486
95.8%
1 634
 
4.2%

Soil_Type40
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size856.5 KiB
0
14664 
1
 
456

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Length

2024-02-08T17:48:59.385860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T17:48:59.454692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14664
97.0%
1 456
 
3.0%

Cover_Type
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.2 KiB
2024-02-08T17:48:59.517669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0000661
Coefficient of variation (CV)0.50001654
Kurtosis-1.2500165
Mean4
Median Absolute Deviation (MAD)2
Skewness0
Sum60480
Variance4.0002646
MonotonicityIncreasing
2024-02-08T17:48:59.587654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 2160
14.3%
2 2160
14.3%
3 2160
14.3%
4 2160
14.3%
5 2160
14.3%
6 2160
14.3%
7 2160
14.3%
ValueCountFrequency (%)
1 2160
14.3%
2 2160
14.3%
3 2160
14.3%
4 2160
14.3%
5 2160
14.3%
6 2160
14.3%
7 2160
14.3%
ValueCountFrequency (%)
7 2160
14.3%
6 2160
14.3%
5 2160
14.3%
4 2160
14.3%
3 2160
14.3%
2 2160
14.3%
1 2160
14.3%

Interactions

2024-02-08T17:48:48.753620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.053977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.976676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.074247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.975236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.924035image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.872740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.073462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.038185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.975301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.853812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.838732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.125135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.054010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.156709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.056144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.007084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.971222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.152985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.130192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.052960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.935113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.920179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.224448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.136507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.240705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.140042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.091484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.054030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.243851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.218135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.127790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.010332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.989555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.305174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.386263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.307436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.223767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.174133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.141283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.334575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.298305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.206942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.090503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.071668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.390753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.470916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.391279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.306208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.257389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.222000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.421492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.387965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.297239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.170688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.388769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.474119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.562100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.484937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.390819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.340664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.537254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.503059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.470918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.374506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.260120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.463163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.567761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.638618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.574472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.490450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.441383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.640038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.602247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.554103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.455932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.342516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.553982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.655524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.733383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.657622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.574601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.523924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.726695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.686878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.646474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.539090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.436863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.637374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.740760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.821286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.741414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.669619image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.623580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.807790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.786620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.726464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.620826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.522635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.707178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.821360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.904702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.822860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.741498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.706800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.906483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.857810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.803968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.690742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.608602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:49.791416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:39.904304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:40.990843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:41.890906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:42.840907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:43.790849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:44.987502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:45.954258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:46.897772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:47.774038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T17:48:48.689343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-08T17:48:59.702364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AspectCover_TypeElevationHillshade_3pmHillshade_9amHillshade_NoonHorizontal_Distance_To_Fire_PointsHorizontal_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysSlopeSoil_Type1Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type2Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type3Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type4Soil_Type40Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Vertical_Distance_To_HydrologyWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4
Aspect1.0000.006-0.0160.618-0.4110.399-0.0780.0240.0550.0540.0910.3370.0690.0450.1010.0360.0350.0640.0330.0400.0930.0270.0460.0280.0790.1030.0390.1070.0340.0340.1140.3460.1990.0620.0760.0710.0560.0350.0860.0600.0660.0920.2430.0930.0520.0370.0000.0000.0000.0600.2100.0570.2110.173
Cover_Type0.0061.0000.004-0.040-0.004-0.090-0.060-0.048-0.0790.0930.1770.5390.1310.2910.2540.1860.0810.2660.1080.0760.2390.0890.0450.2700.2520.1450.0270.0960.0240.0230.3410.5110.3600.1410.2020.1670.0290.1720.0480.1110.4770.4410.2890.3710.1360.2530.0000.0200.0220.0540.5160.2560.3900.817
Elevation-0.0160.0041.0000.0820.0330.2060.5140.3940.601-0.3160.2980.4020.1700.1860.2510.1700.0870.2500.1490.0880.2560.1010.0320.2870.2580.1380.0280.0890.0320.0260.3410.3810.3150.1320.2350.1590.0470.1530.0730.1140.4890.4230.2400.6540.2470.3490.0000.0170.0380.0990.4690.3900.5260.923
Hillshade_3pm0.618-0.0400.0821.000-0.8370.5840.0200.0440.179-0.2940.1250.1870.1120.1000.1180.0260.0530.1070.0520.0410.0620.0620.0230.0530.1310.0370.0000.0530.0480.0620.0890.3840.2550.0660.1080.0840.0320.0380.0300.0250.1220.0970.0380.0680.0710.0290.0000.0000.000-0.0380.1580.0600.1710.222
Hillshade_9am-0.411-0.0040.033-0.8371.000-0.1390.049-0.034-0.0580.0000.0680.4080.0690.0900.1170.0170.0510.0960.0440.0300.0730.0420.0260.0460.1310.0660.0000.0600.0360.0590.1040.3160.2050.0780.0950.0620.0400.0270.0280.0350.1010.0630.0520.0760.1050.0280.0000.0000.000-0.0480.1980.0210.1470.262
Hillshade_Noon0.399-0.0900.2060.584-0.1391.0000.1080.0340.256-0.5390.0770.2950.0730.0940.0460.0290.0440.0930.0490.0440.1000.0480.0110.0440.1240.0950.0080.0760.0020.0440.0510.1180.1690.0570.1100.0460.0310.0360.0560.0220.1120.0990.1640.0510.1610.0450.0000.0000.000-0.1520.1070.0740.2130.213
Horizontal_Distance_To_Fire_Points-0.078-0.0600.5140.0200.0490.1081.0000.1810.432-0.2430.1320.2360.0680.3440.1020.1030.0520.0690.3850.0490.1140.1090.0000.0790.1110.0910.0750.0710.0310.0000.2630.1660.0880.0390.0750.0900.0070.0530.0360.0700.1710.0920.0840.2000.0760.1300.0720.0180.000-0.0040.4380.0980.2360.479
Horizontal_Distance_To_Hydrology0.024-0.0480.3940.044-0.0340.0340.1811.0000.1460.0080.0880.1340.0540.0550.0450.1240.0940.1990.0140.0220.1100.0770.0070.0210.1020.0680.0280.0430.1400.0130.0750.1030.0700.0800.1550.0610.1580.0600.1520.0420.1780.0760.0580.2720.0170.1050.0000.0000.0000.6940.0860.1210.1920.231
Horizontal_Distance_To_Roadways0.055-0.0790.6010.179-0.0580.2560.4320.1461.000-0.2900.1360.2330.0670.1220.1720.0880.0210.1420.0490.0820.1120.0740.0180.1780.1520.0500.0290.1240.0580.0210.3300.2000.2040.1100.1350.1030.0620.1030.0970.1380.2330.1980.1050.1660.1280.1940.0430.0660.000-0.0390.4620.1180.4070.529
Slope0.0540.093-0.316-0.2940.000-0.539-0.2430.008-0.2901.0000.1080.2570.1020.1400.1360.0410.0560.1320.0680.0730.0880.0690.0080.0610.1980.0720.0000.0580.0120.0210.1210.2980.1410.0910.1500.1150.0300.0460.0280.0000.1740.0860.1080.0500.1120.0420.0000.0320.0000.3250.1660.0780.1490.305
Soil_Type10.0910.1770.2980.1250.0680.0770.1320.0880.1360.1081.0000.0600.0210.0160.0260.0120.0060.0300.0000.0000.0290.0090.0000.0200.0320.0170.0000.0000.0000.0000.0450.0390.0320.0180.0300.0290.0000.0060.0000.0000.0320.0290.0350.0240.0120.0310.0000.0000.000-0.0430.0830.0280.1270.226
Soil_Type100.3370.5390.4020.1870.4080.2950.2360.1340.2330.2570.0601.0000.0630.0520.0740.0410.0320.0830.0180.0210.0830.0360.0000.0590.0900.0520.0000.0190.0000.0000.1230.1060.0900.0560.0850.0820.0080.0310.0040.0140.0900.0830.0960.0700.0430.0860.0000.0000.0000.0460.2230.0780.0950.339
Soil_Type110.0690.1310.1700.1120.0690.0730.0680.0540.0670.1020.0210.0631.0000.0180.0280.0130.0070.0310.0000.0000.0310.0100.0000.0210.0340.0180.0000.0000.0000.0000.0480.0410.0340.0200.0320.0310.0000.0070.0000.0000.0340.0310.0370.0260.0130.0330.0000.0000.000-0.0160.0880.0290.1310.044
Soil_Type120.0450.2910.1860.1000.0900.0940.3440.0550.1220.1400.0160.0520.0181.0000.0220.0090.0000.0250.0000.0000.0250.0050.0000.0160.0280.0130.0000.0000.0000.0000.0390.0330.0280.0150.0260.0250.0000.0000.0000.0000.0280.0250.0300.0200.0090.0260.0000.0000.000-0.0170.2370.0230.1110.088
Soil_Type130.1010.2540.2510.1180.1170.0460.1020.0450.1720.1360.0260.0740.0280.0221.0000.0170.0110.0380.0000.0000.0370.0130.0000.0260.0410.0220.0000.0000.0000.0000.0560.0490.0410.0240.0380.0370.0000.0110.0000.0000.0410.0370.0440.0310.0170.0390.0000.0000.0000.0940.1030.0310.2200.125
Soil_Type140.0360.1860.1700.0260.0170.0290.1030.1240.0880.0410.0120.0410.0130.0090.0171.0000.0000.0190.0000.0000.0190.0000.0000.0110.0220.0090.0000.0000.0000.0000.0310.0260.0210.0100.0200.0190.0000.0000.0000.0000.0220.0190.0230.0150.0040.0200.0000.0000.000-0.1260.0580.0180.0530.121
Soil_Type160.0350.0810.0870.0530.0510.0440.0520.0940.0210.0560.0060.0320.0070.0000.0110.0001.0000.0130.0000.0000.0130.0000.0000.0060.0150.0010.0000.0000.0000.0000.0230.0190.0150.0040.0140.0130.0000.0000.0000.0000.0150.0130.0170.0090.0000.0140.0000.0000.000-0.1000.0000.0000.0360.047
Soil_Type170.0640.2660.2500.1070.0960.0930.0690.1990.1420.1320.0300.0830.0310.0250.0380.0190.0131.0000.0020.0050.0420.0160.0000.0290.0460.0260.0000.0040.0000.0000.0640.0550.0460.0280.0430.0420.0000.0130.0000.0000.0460.0420.0500.0350.0200.0440.0000.0000.000-0.2170.1160.0400.0000.126
Soil_Type180.0330.1080.1490.0520.0440.0490.3850.0140.0490.0680.0000.0180.0000.0000.0000.0000.0000.0021.0000.0000.0010.0000.0000.0000.0050.0000.0000.0000.0000.0000.0120.0090.0050.0000.0030.0000.0000.0000.0000.0000.0050.0010.0070.0000.0000.0030.0000.0000.000-0.0380.0950.0000.0440.034
Soil_Type190.0400.0760.0880.0410.0300.0440.0490.0220.0820.0730.0000.0210.0000.0000.0000.0000.0000.0050.0001.0000.0050.0000.0000.0000.0070.0000.0000.0000.0000.0000.0140.0110.0070.0000.0060.0050.0000.0000.0000.0000.0070.0050.0090.0000.0000.0060.0000.0000.000-0.0580.0330.0250.0000.038
Soil_Type20.0930.2390.2560.0620.0730.1000.1140.1100.1120.0880.0290.0830.0310.0250.0370.0190.0130.0420.0010.0051.0000.0160.0000.0290.0460.0250.0000.0030.0000.0000.0630.0540.0460.0270.0430.0410.0000.0130.0000.0000.0460.0420.0490.0350.0200.0440.0000.0000.0000.0070.1150.0390.0640.053
Soil_Type200.0270.0890.1010.0620.0420.0480.1090.0770.0740.0690.0090.0360.0100.0050.0130.0000.0000.0160.0000.0000.0161.0000.0000.0080.0180.0060.0000.0000.0000.0000.0260.0220.0180.0070.0160.0160.0000.0000.0000.0000.0180.0160.0200.0120.0000.0170.0000.0000.000-0.0900.0330.0150.0340.062
Soil_Type210.0460.0450.0320.0230.0260.0110.0000.0070.0180.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000-0.0260.0080.0000.0270.012
Soil_Type220.0280.2700.2870.0530.0460.0440.0790.0210.1780.0610.0200.0590.0210.0160.0260.0110.0060.0290.0000.0000.0290.0080.0001.0000.0320.0160.0000.0000.0000.0000.0450.0380.0320.0180.0300.0290.0000.0050.0000.0000.0320.0290.0340.0240.0120.0300.0000.0000.000-0.0560.1290.0870.0510.100
Soil_Type230.0790.2520.2580.1310.1310.1240.1110.1020.1520.1980.0320.0900.0340.0280.0410.0220.0150.0460.0050.0070.0460.0180.0000.0321.0000.0280.0000.0060.0000.0000.0690.0590.0500.0300.0470.0450.0000.0150.0000.0000.0500.0460.0540.0380.0220.0480.0000.0000.000-0.1710.1020.1120.0070.152
Soil_Type240.1030.1450.1380.0370.0660.0950.0910.0680.0500.0720.0170.0520.0180.0130.0220.0090.0010.0260.0000.0000.0250.0060.0000.0160.0281.0000.0000.0000.0000.0000.0390.0340.0280.0150.0260.0250.0000.0000.0000.0000.0280.0250.0300.0210.0090.0270.0000.0000.0000.0450.0290.0830.0760.089
Soil_Type250.0390.0270.0280.0000.0000.0080.0750.0280.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0920.0110.005
Soil_Type260.1070.0960.0890.0530.0600.0760.0710.0430.1240.0580.0000.0190.0000.0000.0000.0000.0000.0040.0000.0000.0030.0000.0000.0000.0060.0000.0001.0000.0000.0000.0130.0100.0060.0000.0040.0030.0000.0000.0000.0000.0060.0040.0080.0000.0000.0050.0000.0000.000-0.0010.0290.0000.0650.036
Soil_Type270.0340.0240.0320.0480.0360.0020.0310.1400.0580.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0050.0000.0230.009
Soil_Type280.0340.0230.0260.0620.0590.0440.0000.0130.0210.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0020.0000.0210.008
Soil_Type290.1140.3410.3410.0890.1040.0510.2630.0750.3300.1210.0450.1230.0480.0390.0560.0310.0230.0640.0120.0140.0630.0260.0000.0450.0690.0390.0000.0130.0000.0001.0000.0810.0690.0420.0650.0620.0000.0230.0000.0080.0690.0630.0740.0530.0320.0660.0000.0000.000-0.0450.5520.0560.2600.206
Soil_Type30.3460.5110.3810.3840.3160.1180.1660.1030.2000.2980.0390.1060.0410.0330.0490.0260.0190.0550.0090.0110.0540.0220.0000.0380.0590.0340.0000.0100.0000.0000.0811.0000.0590.0360.0560.0540.0000.0190.0000.0050.0600.0550.0640.0460.0270.0570.0000.0000.0000.1020.1480.0510.1690.339
Soil_Type300.1990.3600.3150.2550.2050.1690.0880.0700.2040.1410.0320.0900.0340.0280.0410.0210.0150.0460.0050.0070.0460.0180.0000.0320.0500.0280.0000.0060.0000.0000.0690.0591.0000.0300.0470.0450.0000.0150.0000.0000.0500.0460.0540.0380.0220.0480.0000.0000.0000.0080.4070.0430.1910.151
Soil_Type310.0620.1410.1320.0660.0780.0570.0390.0800.1100.0910.0180.0560.0200.0150.0240.0100.0040.0280.0000.0000.0270.0070.0000.0180.0300.0150.0000.0000.0000.0000.0420.0360.0301.0000.0280.0270.0000.0040.0000.0000.0300.0280.0330.0220.0110.0290.0000.0000.0000.0200.0790.0000.1580.095
Soil_Type320.0760.2020.2350.1080.0950.1100.0750.1550.1350.1500.0300.0850.0320.0260.0380.0200.0140.0430.0030.0060.0430.0160.0000.0300.0470.0260.0000.0040.0000.0000.0650.0560.0470.0281.0000.0430.0000.0140.0000.0000.0470.0430.0510.0360.0210.0450.0000.0000.0000.0460.1180.0340.2230.143
Soil_Type330.0710.1670.1590.0840.0620.0460.0900.0610.1030.1150.0290.0820.0310.0250.0370.0190.0130.0420.0000.0050.0410.0160.0000.0290.0450.0250.0000.0030.0000.0000.0620.0540.0450.0270.0431.0000.0000.0130.0000.0000.0460.0420.0490.0350.0200.0430.0000.0000.0000.0800.1140.0100.2230.138
Soil_Type340.0560.0290.0470.0320.0400.0310.0070.1580.0620.0300.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0150.0000.0380.019
Soil_Type350.0350.1720.1530.0380.0270.0360.0530.0600.1030.0460.0060.0310.0070.0000.0110.0000.0000.0130.0000.0000.0130.0000.0000.0050.0150.0000.0000.0000.0000.0000.0230.0190.0150.0040.0140.0130.0001.0000.0000.0000.0150.0130.0160.0090.0000.0140.0000.0000.000-0.0430.0000.0080.0390.054
Soil_Type360.0860.0480.0730.0300.0280.0560.0360.1520.0970.0280.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0120.0000.0330.016
Soil_Type370.0600.1110.1140.0250.0350.0220.0700.0420.1380.0000.0000.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0050.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000-0.0140.0470.0000.0020.028
Soil_Type380.0660.4770.4890.1220.1010.1120.1710.1780.2330.1740.0320.0900.0340.0280.0410.0220.0150.0460.0050.0070.0460.0180.0000.0320.0500.0280.0000.0060.0000.0000.0690.0600.0500.0300.0470.0460.0000.0150.0000.0001.0000.0460.0540.0380.0220.0480.0000.0000.000-0.0110.0360.0780.0800.152
Soil_Type390.0920.4410.4230.0970.0630.0990.0920.0760.1980.0860.0290.0830.0310.0250.0370.0190.0130.0420.0010.0050.0420.0160.0000.0290.0460.0250.0000.0040.0000.0000.0630.0550.0460.0280.0430.0420.0000.0130.0000.0000.0461.0000.0490.0350.0200.0440.0000.0000.0000.0630.0220.0330.0970.140
Soil_Type40.2430.2890.2400.0380.0520.1640.0840.0580.1050.1080.0350.0960.0370.0300.0440.0230.0170.0500.0070.0090.0490.0200.0000.0340.0540.0300.0000.0080.0000.0000.0740.0640.0540.0330.0510.0490.0000.0160.0000.0000.0540.0491.0000.0410.0240.0510.0000.0000.000-0.0100.1340.0460.1730.040
Soil_Type400.0930.3710.6540.0680.0760.0510.2000.2720.1660.0500.0240.0700.0260.0200.0310.0150.0090.0350.0000.0000.0350.0120.0000.0240.0380.0210.0000.0000.0000.0000.0530.0460.0380.0220.0360.0350.0000.0090.0000.0000.0380.0350.0411.0000.0160.0360.0000.0000.0000.1500.0000.2550.0110.117
Soil_Type50.0520.1360.2470.0710.1050.1610.0760.0170.1280.1120.0120.0430.0130.0090.0170.0040.0000.0200.0000.0000.0200.0000.0000.0120.0220.0090.0000.0000.0000.0000.0320.0270.0220.0110.0210.0200.0000.0000.0000.0000.0220.0200.0240.0161.0000.0210.0000.0000.0000.0270.0600.0180.0920.164
Soil_Type60.0370.2530.3490.0290.0280.0450.1300.1050.1940.0420.0310.0860.0330.0260.0390.0200.0140.0440.0030.0060.0440.0170.0000.0300.0480.0270.0000.0050.0000.0000.0660.0570.0480.0290.0450.0430.0000.0140.0000.0000.0480.0440.0510.0360.0211.0000.0000.0000.0000.0900.1200.0410.1830.323
Soil_Type70.0000.0000.0000.0000.0000.0000.0720.0000.0430.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000-0.0060.0000.0000.0000.000
Soil_Type80.0000.0200.0170.0000.0000.0000.0180.0000.0660.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000-0.0110.0110.0000.0000.000
Soil_Type90.0000.0220.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000-0.0080.0230.0000.0050.000
Vertical_Distance_To_Hydrology0.0600.0540.099-0.038-0.048-0.152-0.0040.694-0.0390.325-0.0430.046-0.016-0.0170.094-0.126-0.100-0.217-0.038-0.0580.007-0.090-0.026-0.056-0.1710.0450.007-0.0010.0330.016-0.0450.1020.0080.0200.0460.0800.028-0.0430.029-0.014-0.0110.063-0.0100.1500.0270.090-0.006-0.011-0.0081.0000.1090.0830.0980.090
Wilderness_Area10.2100.5160.4690.1580.1980.1070.4380.0860.4620.1660.0830.2230.0880.2370.1030.0580.0000.1160.0950.0330.1150.0330.0080.1290.1020.0290.0000.0290.0050.0020.5520.1480.4070.0790.1180.1140.0150.0000.0120.0470.0360.0220.1340.0000.0600.1200.0000.0110.0230.1091.0000.1090.4700.372
Wilderness_Area20.0570.2560.3900.0600.0210.0740.0980.1210.1180.0780.0280.0780.0290.0230.0310.0180.0000.0400.0000.0250.0390.0150.0000.0870.1120.0830.0920.0000.0000.0000.0560.0510.0430.0000.0340.0100.0000.0080.0000.0000.0780.0330.0460.2550.0180.0410.0000.0000.0000.0830.1091.0000.1670.132
Wilderness_Area30.2110.3900.5260.1710.1470.2130.2360.1920.4070.1490.1270.0950.1310.1110.2200.0530.0360.0000.0440.0000.0640.0340.0270.0510.0070.0760.0110.0650.0230.0210.2600.1690.1910.1580.2230.2230.0380.0390.0330.0020.0800.0970.1730.0110.0920.1830.0000.0000.0050.0980.4700.1671.0000.566
Wilderness_Area40.1730.8170.9230.2220.2620.2130.4790.2310.5290.3050.2260.3390.0440.0880.1250.1210.0470.1260.0340.0380.0530.0620.0120.1000.1520.0890.0050.0360.0090.0080.2060.3390.1510.0950.1430.1380.0190.0540.0160.0280.1520.1400.0400.1170.1640.3230.0000.0000.0000.0900.3720.1320.5661.000

Missing values

2024-02-08T17:48:49.972979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-08T17:48:50.252773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
028811302221054102025022188342100000000000000000000000000000000100000000001
1300535114242-161371194215159842001000000000000000000000000100000000000000001
232266314618210922322101072018100000000000000000000000000000001000000000001
332983178661607521982331741248010000000000000000000000001000000000000000001
430803561752637052192271442673100000000000000000000000000100000000000000001
5277229119048082162161343034001000000000000000000000000100000000000000001
629983469108326482032261612097001000000000000000000000010000000000000000001
7321456104885945222272181242563100000000000000000000000000000001000000000001
8312354132101732782272111145181100000000000000000000001000000000000000000001
9297212153656943922042091404991100000000000000000000000000000001000000000001
ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Cover_Type
15110334415771342217712282411463188100000000000000000000000000000000000000001007
151113225104460-22032226234143108001000000000000000000000000000000000001000007
15112322418360131302172331532227100000000000000000000000000000001000000000007
1511334137883916427142312261281423001000000000000000000000000000000000000001007
151143297361342013234742192111251983001000000000000000000000000000000000000000107
151153328321133231251091862271803151001000000000000000000000000000000000000001007
15116345537584192939220229146362010000000000000000000000000000000000000000017
151173279901440411315132402181051503100000000000000000000000000000001000000000007
15118358935794185218682052231551657010000000000000000000000000000000000000000017
151193385345153507636251902161643327001000000000000000000000000000000000000000017